Despite the speedup benefits of such distributed settings, they are prone to so-called Byzantine failures, i.e., when a set of workers return malicious or erroneous computations. Faulty workers may modify their portion of the data and/or models arbitrarily. This can happen on purpose due to adversarial attacks or inadvertently due to hardware or software failures, such as bit-flipping in the memory or communication media. As a result, the distorted gradients can derail the optimization and lead to low test accuracy. Devising training algorithms that are resilient to such failures and which can efficiently aggregate the received gradients has inspired a series of contributions in the area [1]}, [2]}, [3]}, [4]}.
